The present invention relates to wavelength division multiplexing (WDM) optical networks and, more particularly, to virtual network embedding in the WDM optical networks.
Current network infrastructure consists of a large number of heterogeneous proprietary network elements. Any technological evolution within this infrastructure is compounded by the increasing cost of investment since the cycle of design-integrate-deploy needs to be repeated. Thus, such infrastructure ossifies deployment of new services and network innovations.
To enable rapid innovations, recently Software-Defined Optical (SDO) Network architecture is introduced [1], which offers network virtualization through a hypervisor plane to share the same physical substrate among multiple isolated virtual network instances. Network virtualization partitions the functional role of internet providers into Infrastructure Providers (InP) and Service Providers (SP) [2]. InPs manages and maintains hardware and software resources, while SPs offers network services and applications to end users. Network hypervisor acts as a broker between InPs and SPs by mapping the Virtual Network (VN) demands from SPs over physical substrates of InPs. The process of mapping virtual nodes over physical nodes and Virtual Links (VL) over physical routes is referred to as virtual network embedding. To maximize revenue for InPs and resource availability for SPs, One of the challenges in VN embedding is how to maximize the embedded VNs over physical substrates.
In wavelength division multiplexing (WDM) optical networks, upon an arrival of a demand requesting a line rate between end nodes, an optical channel is established by allocating finite amount of spectrum on all fibers along the route. If an intermediate node along the route does not support the wavelength conversion capability, then the channel routed through a node must follow the wavelength continuity constraint, which is defined as an allocation of the same center wavelength to the channel, and the spectral continuity constraint, which is defined as an allocation of the same amount of spectrum to the channel in ingress and egress fibers at the node. To support multiple such channels over a fiber, the spectral conflict constraint must be satisfied, which is defined as an allocation of non-overlapping spectrum to the channels routes over the same fiber. When an optical channel is routed through network equipments and fibers, it accumulates linear and non-linear impairments along the route, which deteriorate the optical signal quality. To ensure a successful transmission of data, at least minimum detectable signal quality must be maintained at a receiver.
Conventionally to address interoperability issues, the International Telecommunication Union Telecommunication Standardization Sector (ITU-T) has standardized fixed channel spacing [3]. The network that follows the ITU-T standard is referred to as fixed grid network as shown in FIG. 1(a). Fixed grid networks may not optimize spectral efficiency while supporting line rates with heterogeneous granularity for ever increasing bandwidth demands. Recently, flexible grid network architecture (as shown in FIG. 1(b)) is introduced in which flexible amount of spectrum is assigned to channels based on the requirements of requested bandwidth, transmission reach, and offered modulation formats. Flexible grid networks highly optimize network spectral efficiency; however, dynamic arrival and departure of channels with heterogeneous spectrum requirements causes fragmentations in spectrum (as shown in FIG. 2), and the network can no longer be in its optimal state. The state of fragmented spectrum in a network is referred to as network fragmentation. Network fragmentation is a serious issue in fixed and flexible grid networks. Spectral fragmentation can block a connection in spite of the availability of sufficient amount of spectrum for the connection, and thus, can deteriorate the network performance.
In a software-defined optical network, an open challenge is how to map virtual networks over a flexible grid transport network while assuring the transmission reach constraints. The problem is referred to as the impairment-aware virtual network embedding over software defined flexible grid networks. The detailed problem definition is as follows.
We are given a physical network Gp(Np, Lp), where Np represents a set of reconfigurable optical add-drop multiplexer (ROADM) nodes and Lp represents a set of fibers. A variable rate transponder at each node consists of an optical multicarrier modulator which can modulate subcarriers using a set Z of electrical modulation formats to vary the line rate, where Z={PM-BPSK, PM-QPSK, PM-16QAM}. Here, PM-BPSK stands for polarisation multiplexing binary phase shift-keying. PM-QPSK stands for polarisation multiplexing quadrature phase-shift keying. PM-16QAM stands for polarisation multiplexing 16 quadrature amplitude modulation.
Each modulation format z in Z can transmit a channel up to Dz km with spectral efficiency Sz b/s/Hz. VN demands (Gv(Nv, Lv), RLv) arrives in the network according to incremental traffic model [4] in which a permanent VN is provisioned on a one-by-one basis, where Nv denotes a set of virtual nodes, Lv denotes a set of virtual links, and RLv denotes a set of requested line rates. rij (εRLv) represents a requested line rate between a virtual link (i,j) in Lv. The network offers Y GHz of total spectrum to support the demands. We need to find mapping of VNs over the physical substrate such that the number of embedded VNs is maximized. Mapping of a VN over physical substrate requires to find routing of virtual links over physical routes, selection of modulation formats for virtual links, and wavelength and spectrum assignment to virtual links.
In [5], the authors formulated the VN embedding problem in fixed and flexible grid networks using Integer Linear Program for static traffic model in which a set of VN demands can be provisioned in any order. The solutions in [5] ignore the physical layer impairments while provisioning VN demands. Furthermore, the proposed formulations may not guarantee an optimal solution and are not scalable for a large problem instances.